Robust linear precoder designs for multi-cell downlink transmission

ABSTRACT

Methods and systems for optimizing the utilities of receiver devices in a wireless communication network are disclosed. Precoder design formulations that maximize a minimum worst-case rate or a worst-case sum rate are described for both full base station cooperation and limited base station cooperation scenarios. In addition, optimal equalizers are also selected to optimize the worst-case sum rate.

RELATED APPLICATION INFORMATION

This application is a divisional of U.S. patent application Ser. No.12/878,258, filed on Sep. 9, 2010, which in turn claims priority to U.S.Provisional Application No. 61/240,769, filed on Sep. 9, 2009, thecontent of which is incorporated herein by reference.

BACKGROUND

1. Technical Field

The present invention relates to optimizing the utility of devices in awireless communication network and, more particularly, to thedetermination of precoders that optimize the network utility.

2. Description of the Related Art

The increasing demand for accommodating growing numbers of users withinwireless networks has a tendency to limit the signal quality in suchnetworks due to interference generated by serving a large number ofusers. A useful approach for mitigating the interference in downlinktransmissions is to equip the base stations with multiple transmitantennas and employ transmit precoding. Such precoding exploits thespatial dimension to ensure that the signals intended for differentusers remain easily separable at their designated receivers. To enableprecoded transmission, the base stations should acquire the knowledge ofchannel states or channel state information (CSI).

Many existing works have addressed the beamforming design problem byassuming that each base station communicates with its respectiveterminals independently. In such a framework, inter-cell interference issimply regarded as additional background noise and the design of thebeamforming vectors is performed on a per-cell basis only. Other worksassume that both data and channel state information of all users couldbe shared in real-time, so that all base stations can act as a uniquelarge array with distributed antenna elements. These works employ jointbeamforming, scheduling and data encoding to simultaneously servemultiple co-channel users. Moreover existing works also assume that thechannel state information is perfectly known to the sources.

SUMMARY

Exemplary embodiments of the present invention enable the robustoptimization of user-utilities by designing precoders that account forinter-cell interference between users of different cells and intra-cellinterference between users in a common cell. Furthermore, exemplaryembodiments can also consider channel states and correspondinguncertainty regions of channels received by users that are intended forother users in the same cell or in other cells to ensure that thedesigned precoders provide optimal utilities.

One exemplary embodiment is directed to a method for optimizing theutility of receiver devices in a wireless communication network. In themethod, information indicative of channel states corresponding tochannels received by a set of receiver devices can be obtained. Inaddition, a precoding matrix can be determined by maximizing, for theset of receiver devices, a utility estimate corresponding to a minimumreceiver rate within a set of rates corresponding to a bounded set ofchannel estimation errors determined by considering a set of channelsincluding channels received by the set of receiver devices from basestations other than a base station servicing the set of receiverdevices. Further, beamforming signals generated in accordance with thedetermined precoding matrix can be transmitted to the receiver devices.

An alternative exemplary embodiment is drawn towards a method foroptimizing the utility of receiver devices in a wireless communicationnetwork. The method may include obtaining information indicative ofchannel states of channels received by a set of receiver devices.Additionally, a precoding matrix can be determined by maximizing, forthe set of receiver devices, a utility estimate corresponding to aminimum receiver weighted-sum rate within a set of rates correspondingto a bounded set of channel estimation errors determined by consideringa set of channels including channels received by the set of receiverdevices from base stations other than a base station servicing the setof receiver devices. Thereafter, beamforming signals generated inaccordance with the determined precoding matrix can be transmitted tothe receiver devices.

Another exemplary embodiment is directed to a system for optimizing theutility of receiver devices in a wireless communication network. Thesystem may comprise a set of base stations that are configured tojointly apply a beamforming design to transmit beamforming signals tosets of receiver devices respectively served by the base stations. Here,a precoding matrix in the design for each base station can be determinedby maximizing a utility estimate corresponding to a minimum receiverrate or a minimum receiver weighted-sum rate within a set of ratescorresponding to a bounded set of channel estimation errors determinedby considering a set of channels including channels received by the setof receiver devices from base stations other than a base station servingthe set of receiver devices.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block diagram of an exemplary network communication systemexhibiting inter-cell and intra-cell interference.

FIG. 2 is a block diagram of an exemplary network communication systemexhibiting inter-cell and intra-cell interference and illustratinguncertainty regions of received channels.

FIG. 3 is a block/flow diagram of an exemplary method for optimizing theutility of receiver devices in a wireless communication network bymaximizing a worst-case minimum rate.

FIG. 4 is a block/flow diagram of an exemplary method for determiningprecoding matrices in accordance with a robust minimum rate optimizationscheme in a full base station cooperation scenario.

FIG. 5 is a block/flow diagram of an exemplary method for successivelydetermining precoding matrices in accordance with a robust minimum rateoptimization scheme in a limited base station cooperation scenario.

FIG. 6 is a block/flow diagram of an exemplary method for greedilydetermining precoding matrices in accordance with a robust minimum rateoptimization scheme in a limited base station cooperation scenario.

FIG. 7 is a block/flow diagram of an exemplary method for optimizing theutility of receiver devices in a wireless communication network bymaximizing a worst-case weighted sum-rate.

FIG. 8 is a block/flow diagram of an exemplary method for determiningprecoding matrices in accordance with a robust weighted sum-rateoptimization scheme.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Exemplary embodiments of the present invention address the problem ofdesigning beamforming vectors or beamformers for a multi-cell,multi-user wireless communication system. Coordinated informationprocessing by the base stations of multi-cell wireless networks enhancesthe overall quality of communication in the network. Such coordinationsfor optimizing any desired network-wide quality of service (QoS)typically involve the acquisition and sharing of some channel stateinformation (CSI) by and between the base stations. With perfectknowledge of channel states, the base stations can adjust theirtransmissions to achieve a network-wide QoS optimality. In practice,however, the CSI can be obtained only imperfectly. As a result, due tothe uncertainties involved, the network is not guaranteed to benefitfrom a globally optimal QoS. Nevertheless, if the channel estimationperturbations are confined within bounded regions, the QoS measure willalso lie within a bounded region. Therefore, by exploiting the notion ofrobustness in the worst-case sense, worst-case QoS guarantees for thenetwork can be achieved and asserted.

Embodiments of the present invention described herein below can employ amodel for noisy channel estimates that assumes that estimation noiseterms lie within known hyper-spheres. Embodiments include lineartransceivers that optimize a worst-case QoS measure in downlinktransmissions. In particular, embodiments can maximize the worst-caseweighted sum-rate of the network and the minimum worst-case rate of thenetwork. Several centralized or fully cooperative and distributed (withlimited cooperation) processes and systems, entailing different levelsof complexity and information exchange among the base stations,described herein below can be employed to implement such transceiverdesigns.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1, a portion of awireless communication system 100 in which embodiments of the presentinvention can be implemented is illustrated. Each base-station (BS) orsource 102, 104 servicing cells 106, 108, respectively, in the system100 is modeled as having access to imperfect channel state information.All base stations can be equipped with multiple transmit antennas andeach mobile user 110, 112 in the system can be equipped with a singlereceive antenna. Each base station in the system can employ linearbeamforming to serve each one of its scheduled users or mobile deviceson any resource slot. Here, each scheduled user is assumed to be servedby only one base station. The association of each user to a particularbase station (its serving base station) can be pre-determined. Further,each user can receive a useful (desired) signal 114 from its servingbase station in addition to interference from its serving base station(116) as well as from adjacent base stations (118). The interferenceincludes all signals transmitted in the same resource slot as thedesired signal but which are intended for the other users. It should beunderstood that a resource slot can be a time, frequency and/or codeslot.

Each BS or source in the system 100 has access to channel stateinformation that can be limited in that it is not global and imperfectin that it is subject to errors. In addition, some side informationabout the nature of errors can be available and limited informationexchange (signaling) between base-stations is possible. According toaspects of the present principles, beam vectors can be designed for acluster or set of coordinating base stations based on a given set ofchannel estimates, which ensure a robust performance. For example, inthe case that the errors lie in a bounded region, the designed beamvectors can maximize a utility that is guaranteed for all possibleerrors, whereas in the case that the errors are realizations drawn fromknown distributions, the designed beam vectors can maximize a utilityunder probabilistic guarantees. As understood by those of skill in theart, a utility measure can be based on and directly correlated to aminimum receiver rate or a weighted sum of receiver rates.

FIG. 2 provides a more detailed system 200 in which embodiments can beimplemented. As depicted in FIG. 2, all transmitters 202, 203 areequipped with multiple transmit antennas 212 and the users are eachequipped with a single receive antenna 214. Every transmitter can beconfigured to employ linear beamforming to serve each one of itsscheduled users. Each user, in turn, receives useful (desired) signals206 from one transmitter and also receives interference 210 from itsserving transmitter as well as adjacent transmitters. As noted above,the interference can include all signals, transmitted in the sameresource slot as the desired signal but which are intended for otherusers.

Each transmitter or source in system 200 employs an estimate of each oneof its “outgoing channels,” which are the channels between a transmitterand all users. Each transmitter also employs an estimate of all the“incoming channels” seen by each one of the users served by it. In otherwords, the incoming channels are the channels seen by each served userfrom all transmitters. However, in practice, these estimates areimperfect. In particular, the true channel between any source andreceiver is equal to the sum of the corresponding estimate and an errorvector. According to exemplary aspects, the actual error vector isunknown to the source but the source is aware of the region, referred toas the uncertainty region, in which the error vector lies. In FIG. 2,the uncertainty regions are represented by ellipsoids 208. It should benoted that any reference to a “base station” herein can correspond toany one or more base stations in systems 100 and/or 200.

As indicated above, many existing works have addressed the beamformingdesign problem by assuming that each base station communicates with itsrespective terminals independently. Further, other works assume thatboth data and channel state information of all users could be shared inreal-time, so that all base stations can act as a unique large arraywith distributed antenna elements and employ joint beamforming,scheduling and data encoding to simultaneously serve multiple co-channelusers. In practice, however, only a much lower level of coordinationamong base stations is feasible and it is much more reasonable to assumethat each user is served by only one base station. Moreover, asindicated above, existing works also assume that the channel stateinformation is perfectly known to the sources, which unfortunately doesnot hold in practice.

Embodiments described herein apply to a more general model of multi-cellwireless networks, which hitherto has not been investigated for robustoptimization, and treat the problem of joint robust transmissionoptimization for multiple or all cells. The significance of suchmulti-cell transmission optimization is that it incorporates the effectsof inter-cell interferences, which are ignored when the cells optimizetheir transmissions independently. Furthermore, a practical constraintwhich forbids real-time data sharing among base stations can beincorporated so that each user can be served by only one base station.

Embodiments can design beam vectors for a cluster or set of coordinatingtransmitters (base stations) based on a given set of channel estimatesand their uncertainty regions, which ensure a robust performance. Inparticular, the designed beam vectors can maximize a utility that isguaranteed for all possible errors within the respective uncertaintyregions. For example, embodiments can design beam vectors by optimizingthe worst-case weighted sum-rate or the minimum worst-case rate. For anygiven set of beams, user weights and channel estimates, the worst-caseweighted sum-rate is the weighted sum-rate obtained when the errorsassume their worst-possible values within the respective uncertaintyregions. Thus, by maximizing the worst-case weighted sum-rate over thechoice of beams, embodiments can obtain a robust choice of beam vectorsthat yields the best possible weighted sum-rate that is guaranteed forall possible errors within the respective uncertainty regions.

Transmission Model

A detailed transmission model is now provided to better illustrateaspects of the present principles. In the model, a multi-cell networkincludes M cells, where each cell has one base station that serves Kusers. The BSs are equipped with N transmit antennas and each useremploys one receive antenna. B_(m) denotes the BS of the m^(th) cell andU_(m) ^(k) denotes the k^(th) user in the m^(th) cell for mε{1, . . . ,M} and kε{1, . . . , K}. Quasi-static flat-fading channels are assumedand the downlink channel from B_(n) to U_(m) ^(k) is denoted by h_(m,n)^(k)εC^(1×N).

Let x_(m)=[x_(m) ¹, . . . , x_(m) ^(K)]^(T)εC^(K×1) denote theinformation stream of B_(m), intended for serving its designated usersvia spatial multiplexing. It can be assumed that E[x_(m)x_(m) ^(H)]=I.Prior to transmission by B_(m), the information stream x_(m) is linearlyprocessed by the precoding matrix F_(m)εC^(N×K). The k^(th) column ofF_(m) is denoted by w_(m) ^(k)εC^(N×1), which is the beam carrying theinformation stream intended for user U_(m) ^(k). By defining f_(m)^(k)εC\{0} as the single-tap receiver equalizer deployed by U_(m) ^(k),the received post-equalization signal at U_(m) ^(k) is given by

$\begin{matrix}{{y_{m}^{k}\overset{\Delta}{=}{\frac{1}{f_{m}^{k}}( {{\sum\limits_{n = 1}^{M}{h_{m,n}^{k}F_{n}x_{n}}} + z_{m}^{k}} )}},} & (1)\end{matrix}$

where z_(m) ^(k): CN(0,1) accounts for the additive white complexGaussian noise. It can be assumed that the users deploy single-userdecoders for recovering their designated messages while suppressing themessages intended for other users as Gaussian interference. Therefore,the signal to interference-plus-noise ratio (SINR) of user U_(m) ^(k)(with the optimal equalizer) is given by

$\begin{matrix}{{SINR}_{m}^{k}\overset{\Delta}{=}{\frac{{{h_{m,m}^{k}w_{m}^{k}}}^{2}}{{\sum\limits_{l \neq k}{{h_{m,m}^{k}w_{m}^{l}}}^{2}} + {\sum\limits_{n \neq m}{\sum\limits_{l}{{h_{m,n}^{k}w_{n}^{l}}}^{2}}} + 1}.}} & (2)\end{matrix}$

Also, M{tilde over (S)}E_(m) ^(k) is defined as the mean square-error(MSE) of user U_(m) ^(k) when it deploys the equalizer l_(m) ^(k) and isgiven by

$\begin{matrix}\begin{matrix}{{M\overset{\sim}{S}E_{m}^{k}} \equiv {E\lbrack {{y_{m}^{k} - x_{m}^{k}}}^{2} \rbrack}} \\{= \frac{1}{{f_{m}^{k}}^{2}}} \\{( {{{{h_{m,m}^{k}w_{m}^{k}} - f_{m}^{k}}}^{2} + {\sum\limits_{l \neq k}{{h_{m,m}^{k}w_{m}^{l}}}^{2}} +} } \\ {{\sum\limits_{n \neq m}{\sum\limits_{l}{{h_{m,n}^{k}w_{m}^{l}}}^{2}}} + 1} )\end{matrix} & (3)\end{matrix}$

MSE_(m) ^(k) is further defined as the MSE corresponding to the minimummean-square error (MMSE) equalizer which minimizes the MSE over allpossible equalizers, i.e.,

$\begin{matrix}{{MSE}_{m}^{k} \equiv {\min\limits_{f_{m}^{k}}{M\overset{\sim}{S}E_{m}^{k}}}} & (4)\end{matrix}$

It can be assumed that user U_(m) ^(k) knows its incoming channels,{h_(m,n) ^(k) }_(n=1) ^(M), perfectly. In contrast, each BS is assumedto acquire only noisy estimates of such channels corresponding to itsdesignated receivers, i.e., B_(m) knows the channels {h_(m,n)^(k)}_(k,n) imperfectly. {tilde over (h)}_(m,n) ^(k) denotes the noisyestimate of the channel h_(m,n) ^(k) available at B_(m) (and possiblyother BSs via cooperation) and the channel estimation errors, which areunknown to the BSs, is defined as

D _(m,n) ^(k)

h _(m,n) ^(k) −{tilde over (h)} _(m,n) ^(k) , ∀m,nε{1, . . . , M}, and∀kε{1, . . . , K}.  (5)

It can be assumed that such channel estimation errors are bounded andconfined within an origin-centered hyper-spherical region of radiusε_(m,n) ^(k), i.e., |D_(m,n) ^(k)∥₂≦ε_(m,n) ^(k). It is also noted thatall the results derived in the sequel can be readily extended to thecase where the uncertainty regions are bounded hyper-ellipsoids. In thesequel, for any matrix A, ∥A∥₂ is used to denote matrix A's Frobeniusnorm.

Problem Statement

Using the transmission model described above, a formal problem statementis now provided, which details the goal of optimizing network-wideperformance measures through the design of precoding matrices {F_(m)}and/or receiver equalizers {f_(m) ^(k)} achieved by various exemplaryembodiments. Such optimization hinges on the accuracy of channelestimates available at the BSs. Due to the uncertainties about channelsestimates, the notion of robust optimization in the worst-case sense isadopted. The solution of the worst-case robust optimization is feasibleover the entire uncertainty region and provides the best guaranteedperformance over all possible CSI errors.

Based on this notion of robustness, two rate optimization problems areconsidered. One pertains to maximizing the worst-case weighted sum-rateof the multi-cell network and the other one seeks to maximize theminimum worst-case rate in the network. Both optimizations are subjectto individual power constraints for the BSs. Let R_(m) ^(k) denote therate assigned to user U_(m) ^(k) and the power budget for the BS B_(m)is denoted by P_(m). Also, P is defined as the vector of power budgets,P

[P₁, . . . , P_(M)].

First the robust max-min rate problem, which aims to maximize theminimum worst-case rate of the network subject to the power budget P, isconsidered. Because the users can be assumed to deploy single-userdecoders, R_(m) ^(k) can be defined in terms of the SINR of user U_(m)^(k): R_(m) ^(k)=log(1+SINR_(m) ^(k)). Therefore, the robust max-minrate problem can be posed as

$\begin{matrix}{{S(P)}\overset{\Delta}{=}\{ \begin{matrix}\max\limits_{\{ F_{m}\}} & {\min\limits_{k,m}\min\limits_{\{ D_{m,n}^{k}\}}} & {SINR}_{m}^{k} \\{s.t.} & {{F_{m}}_{2}^{2} \leq P_{m}} & {\;_{\forall m}.}\end{matrix} } & (6)\end{matrix}$

As the second problem, optimization of the worst-case weighted sum-rateof the network is considered. For a given set of positive weightingfactors {α_(m) ^(k)}, where α_(m) ^(k) is the weighting factorcorresponding to the rate of user U_(m) ^(k), this problem is formalizedas

$\begin{matrix}{{R(P)}\overset{\Delta}{=}\{ \begin{matrix}\max\limits_{\{ F_{m}\}} & {\min\limits_{\{ D_{m,n}^{k}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}R_{m}^{k}}}}} \\{s.t.} & {{F_{m}}_{2}^{2} \leq {P_{m}\mspace{14mu}}_{\forall m}}\end{matrix} } & (7)\end{matrix}$

Solving these problems yields the design of the precoder matrices. Itshould be noted that for a given set of precoders {F_(m)} and channelrealizations {h_(m,n) ^(k)}, the MMSE equalization factors {f_(m) ^(k)}can be obtained in closed form.

It should also be noted that the joint design of the optimal precodersmay involve having each BS acquire global CSI, which necessitates fullcooperation (full CSI exchange) among the BSs. Such centralizedprocesses are employed in certain exemplary embodiments described hereinbelow with the assumption that such full cooperation is feasible. Inpractice, however, full cooperation might not be implementable. In suchcases, distributed processes, also described herein below, that entaillimited cooperation among the BSs can be employed. Some of the exemplaryembodiments that utilize distributed processes yield the sameperformance as their centralized counterparts.

Robust Max-Min Rate Optimization

Referring now to FIG. 3 with continuing reference to FIGS. 1 and 2, amethod 300 for optimizing the utility of receiver devices in a wirelesscommunication system is illustrated. In particular, the method 300generally implements a robust max-min rate optimization and can beperformed in a variety of ways, as discussed further herein below.Method 300 can be performed by a base station 102, 202 or a controlcenter, which may itself be a designated base station 102, 202, if fullcooperation between base stations is feasible. Alternatively, if onlylimited cooperation is feasible, then the method can be performedindependently by each base station, as discussed further herein below.

The method 300 may begin at step 302 in which a base station or acontrol center may obtain a set of estimates of channel statescorresponding to channels received by a set of receiver devices. Theseestimates can be received in the form of feedback from the receiverdevices. The feedback from each receiver device can be limited to a fewbits. Thus, each receiver device may have to quantize each one of thechannel estimates available to it. Consequently, the base station orcontrol center may have access to channel estimates that are corruptedby quantization errors. As indicated above, the channel stateinformation may correspond to {h_(m,n) ^(k)}_(n=1) ^(M), which includesthe channel h_(m,m) ^(k) received by the receiver from the base stationB_(m) servicing the receiver in addition to the channels h_(m,n) ^(k)(n≠m) received by the receiver from base stations B_(n) servicing cellsother than cell m. In a fully cooperative scenario, each base stationmay communicate the respective channel state information for receiversor users in the corresponding cell to the other base stations in thenetwork or to a control center. Alternatively, in a limited cooperationscenario, while each base station can obtain channel information{h_(m,n) ^(k)}_(n=1) ^(M) for receivers in its own cell, each basestation can communicate prospective precoding matrices to other bases tooptimize receiver utility in the network, as discussed in more detailherein below.

At step 304, a base station or control center can determine a precodingmatrix for the set of receiver devices by maximizing a minimum worstcase rate of the network. For example, as discussed above with regard toequation (6), the precoding matrices {F_(m)} for the base stations inthe network can be determined in accordance with a power budget bysolving the following:

${S(P)}\overset{\Delta}{=}\{ \begin{matrix}\max\limits_{\{ F_{m}\}} & {\min\limits_{k,m}\min\limits_{\{ D_{m,n}^{k}\}}} & {SINR}_{m}^{k} \\{s.t.} & {{F_{m}}_{2}^{2} \leq P_{m}} & {\;_{\forall m}.}\end{matrix} $

As noted above, R_(m) ^(k)=log(1+SINR_(m) ^(k)). Thus, for base stationm, the term

$\min\limits_{k,m}{\min\limits_{\{ D_{m,n}^{k}\}}{SINR}_{m}^{k}}$

is an example of a utility estimate corresponding to the minimumreceiver rate

$\min\limits_{k,m}R_{m}^{k}$

within a set of rates corresponding to a bounded set of channelestimation errors {D_(m,n) ^(k)}. Furthermore, the estimate can bedetermined by considering a set of channels {h_(m,n) ^(k)}_(n=1) ^(M)including channels h_(m,n) ^(k) (n≠m) received by the set of receiverdevices from base stations B_(n) (n≠m) other than a base station B_(m)servicing the set of receiver devices. For example, as noted above andbelow with respect to various exemplary embodiments, {h_(m,n) ^(k)}_(n=)^(M) can be used to determine values of SINR_(m) ^(k) to solve equation(6).

It should be noted that, at step 304, in the full cooperation scenario,the control center can determine precoding matrices for each basestation using channel state information for receivers in each cellserviced by the base stations and can assign the precoding matrices tothe base stations to enable them to generate optimized, beamformingsignals for transmission to the receiver. Alternatively, in the fullcooperation scenario, each base station may independently determinetheir own precoding matrix using the same methods, where each basestation can receive channel state information from each other basestation, determine the precoding matrices for the entire network andapply the precoding matrix. Various exemplary embodiments that can beemployed to implement step 304 are discussed further herein below.

At step 306, each base station can transmit beamforming signalsgenerated in accordance with the determined precoding matrix to theirown respective receiver devices in their respective cell.

Returning to step 304, one or more base stations can implement step 304by solving equation (6) via power optimization or via MSE optimization.To better illustrate how embodiments can implement the poweroptimization approach, a solution to equation (6) is presented in whicheach base station is assumed to serve only one user in its respectivecell.

Single-User Cells (K=1)

Under the assumption that each BS is serving one user, i.e., K=1, thedownlink transmission model essentially becomes equivalent to amulti-user Gaussian interference channel with M transmitters and Mrespective receivers. For the ease of notation the superscript k isomitted in the subsequent analysis and discussions with regard tosingle-user cells. When K=1 the precoder of BS B_(m) consists of onlyone column vector which is referred to by w_(m). For the given channelestimates {{tilde over (h)}_(m,n)}, sinr_(m) is defined as sinr_(m)

min_({D) _(m,n) _(}) SINR_(m) as the worst-case (smallest) SINR_(m) overthe uncertainty regions.

By introducing a slack variable a>0, the epigraph form of the robustmax-min rate optimization problem S(P) given in (6) is given by

$\begin{matrix}{{S(P)} = \{ \begin{matrix}\max\limits_{{\{ w_{m}\}},a} & a & \; \\{s.t.} & {{sinr}_{m} \geq a} & {{\forall m},} \\\; & {{w_{m}}_{2}^{2} \leq P_{m}} & {\forall{m.}}\end{matrix} } & (8)\end{matrix}$

The closed-form characterization of sinr_(m) can be found by recalling(2) to obtain

$\begin{matrix}\begin{matrix}{{sinr}_{m} = {\min\limits_{\{ D_{m,n}\}}\frac{{{h_{m,m}w_{m}}}^{2}}{{\sum\limits_{n \neq m}{{h_{m,n}w_{n}}}^{2}} + 1}}} \\{{= \frac{\min\limits_{D_{m,m}}{{h_{m,m}w_{m}}}^{2}}{{\sum\limits_{n \neq m}{\max\limits_{D_{m,n}}{{h_{m,n}w_{n}}}^{2}}} + 1}},}\end{matrix} & (9)\end{matrix}$

where the second equality holds by noting that finding the worst-caseSINR_(m) can be decoupled into finding the worst-case (smallest)numerator term and the worst-case (largest) denominator terms. In orderto further simplify sinr_(m), the result of the following lemma can beused. The proof is omitted for brevity purposes.

Lemma 1 For any given hεC^(1×N), wεC^(N×1), εεR⁺, and positive definitematrix Q; g_(min) and g_(max) defined as

$g_{\min}\overset{\Delta}{=}\{ {{\begin{matrix}\min\limits_{x} & {{{hw} + {xw}}}^{2} \\{s.t.} & {{\sqrt{{xQx}^{H}} \leq ɛ},}\end{matrix}{and}g_{\max}}\overset{\Delta}{=}\{ \begin{matrix}\max\limits_{x} & {{{hw} + {xw}}}^{2} \\{s.t.} & {{\sqrt{{xQx}^{H}} \leq ɛ},}\end{matrix} } $

are given by

g _(min)=|(|hw|−ε√{square root over (w^(H) Q ⁻¹ w)})⁺|², and g_(max)=∥hw|+ε√{square root over (w^(H) Q ⁻¹ w)}|²,

where (x)⁺=max{0,x}, ∀xεR.

By recalling (9) and invoking the result of the Lemma 1 for the choiceof Q=I, sinr_(m) can be further simplified as

$\begin{matrix}{{sinr}_{m} = {\frac{{( {{{{\overset{\sim}{h}}_{m,m}w_{m}}} - {ɛ_{m,m}{w_{m}}_{2}}} )}^{2}}{{\sum\limits_{n \neq m}{{{{{\overset{\sim}{h}}_{m,n}w_{n}}} - {ɛ_{m,n}{w_{n}}_{2}}}}^{2}} + 1}.}} & (10)\end{matrix}$

The relevant scenarios are where ∀m, ∥{tilde over (h)}_(m,m)∥₂≧ε_(m,m),so that S(P)>0. Given the closed-form characterization of sinr_(m),solving S(P) can be facilitated by solving a power optimization problemdefined as

$\begin{matrix}{{P( {P,a} )}\overset{\Delta}{=}\{ \begin{matrix}\min\limits_{{\{ w_{m}\}},b} & b \\{{s.t.\; {sinr}_{m}} \geq a} & {{\forall m},} \\{\frac{{w_{m}}_{2}}{\sqrt{P_{m}}} \leq b} & {\forall m}\end{matrix} } & (11)\end{matrix}$

The connection between S(P) and P(P,a) is established in the followinguseful theorem. The proof is simple and hence omitted for brevity.

Theorem 1 For any given power budget P, P(P,a) is strictly increasingand continuous in a at any strictly feasible a and is related to S(P)via

P(P,S(P))=1.

Strict monotonicity and continuity of P(P,a) in a at any strictlyfeasible a provides that there exists a unique a* satisfying P(P,a*)=1.Hence, taking into account Theorem 1 establishes that S(P) can be solvedby finding a* that satisfies P(P,a*)=1. Due to monotonicity andcontinuity of P(P,a), finding a* can be implemented via a simpleiterative bi-section search. Each iteration involves solving P(P,a) fora different value of a. P(P,a) can be cast as a convex problem with acomputationally efficient solution.

Theorem 2 Problem P(P,a) can be posed as a semidefinite programming(SDP) problem.

The proof is omitted for brevity purposes.

To employ such a procedure for solving S(P), the BSs should be fullycooperative such that each BS can acquire estimates of all network-widechannel states.

Multi-User Cells (K>1)

Downlink transmissions serving more than one user in each cell (K>1) arenow considered. The major difference between the analysis for multiusercells and that of single-user cells arises from differentcharacterizations of their corresponding worst-case SINRs. By definingsinr_(m) ^(k) as the worst-case SINR_(m) ^(k) in (2), sinr_(m) ^(k) isobtained as

$\begin{matrix}{{sinr}_{m}^{k} = {\min\limits_{\{ D_{m,n}^{k}\}}{\frac{{{h_{m,m}^{k}w_{m}^{k}}}^{2}}{{\sum\limits_{l \neq k}{{h_{m,m}^{k}w_{m}^{l}}}^{2}} + {\sum\limits_{n \neq m}{\sum\limits_{l}^{\;}{{h_{m,n}^{k}w_{n}^{l}}}^{2}}} + 1}.}}} & (12)\end{matrix}$

Unlike the single-user setup, when K>1, the uncertainty regions of thenumerator and the summands of the denominator of sinr_(m) ^(k) are notdecoupled. Therefore, finding sinr_(m) ^(k) cannot be decoupled intofinding the worst-case numerator and the terms in the denominatorindependently. To the knowledge of the inventors, handling constraintson such worst-case SINR s even in single-cell downlink transmissions isnot mathematically tractable and the robust design of linear precodersfor these systems is carried out suboptimally. In the sequel suboptimalapproaches are also proposed for solving the robust max-min rateoptimization in multi-cell networks.

Two suboptimal approaches can be employed for solving S(P). In the firstapproach, a lower bound on the worst-case SINR is found and, in theformulation of S(P), each worst-case SINR is replaced with itscorresponding lower bound. Similar to the single-user cells setupdiscussed above, this approximate problem can be solved efficientlythrough solving a counterpart power optimization problem. In the secondapproach, the robust max-min rate optimization problem can be convertedinto a robust min-max MSE optimization problem and an upper bound on themaximum worst-case MSE, which in turn provides a lower bound on theminimum worst-case rate, can be found. Each approach is described hereinbelow.

Solving Via Power Optimization

Returning to method 300, exemplary embodiments can determine theprecoding matrices at step 304 by maximizing, at step 310, a slackvariable corresponding to a lower bound of an estimate corresponding tothe minimum worst-case receiver rate under a power constraint. Forexample, the worst-case value of SINR_(m) ^(k), which is denoted bysinr_(m) ^(k), is not mathematically tractable. Consequently, to solve(6), lower bounds on the worst-case SINR s are found as follows. sinr_(m) ^(k) is defined as

${{\overset{\_}{sinr}}_{m}^{k}\overset{\Delta}{=}\frac{\min\limits_{D_{m,m}^{k}}{{h_{m,m}^{k}w_{m}^{k}}}^{2}}{{\max\limits_{D_{m,m}^{k}}{\sum\limits_{l \neq k}^{\;}{{h_{m,m}^{k}w_{m}^{l}}}^{2}}} + {\sum\limits_{n \neq m}^{\;}{\max\limits_{D_{m,n}^{k}}{\sum\limits_{l}^{\;}{{h_{m,n}^{k}w_{n}^{l}}}^{2}}}} + 1}},$

where, clearly, sinr _(m) ^(k)≧sinr_(m) ^(k). By applying Lemma 1,

$\begin{matrix}{{{\overset{\_}{sinr}}_{m}^{k} = \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}\; {h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,m}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}{\max\limits_{D_{m,n}^{k}}\; {h_{m,n}^{k}{F_{n}( F_{n} )}^{H}( h_{m,n}^{k} )^{H}}}} + 1}\end{matrix}}},} & (13)\end{matrix}$

where Y_(m,k) is defined as Y_(m,k)

[w_(m) ¹ . . . , w_(m) ^(k−1),w_(m) ^(k+1), . . . , w_(m) ^(K)].

By introducing the slack variable a and invoking the lower bounds on theSINRs given in (13), a lower bound on the robust max-min rate isobtained as follows:

$\begin{matrix}{{S_{1}(P)} \equiv \{ \begin{matrix}\max\limits_{{\{ F_{m}\}},a} & a \\{{s.t.\; {\overset{\_}{sinr}}_{m}^{k}} \geq a} & {{\forall m},k,} \\{{F_{m}}_{2}^{2} \leq P_{m}} & {\forall m}\end{matrix} } & (14)\end{matrix}$

To maximize the slack variable in step 304, the base station can, atstep 312, iteratively solve a power optimization problem to obtain avalue of the slack variable that solves the slack variable maximizationproblem by determining whether slack variable values result in apre-determined solution of the power optimization problem that iscorrelated to the solution to the slack variable maximization problem.For example, similar to the single-user scenario discussed above,solving S₁(P) can be carried out by alternatively solving a poweroptimization problem in conjunction with a linear bi-section search. Thepower optimization of interest with per BS power constraints is given by

$\begin{matrix}{{P_{1}( {P,a} )}\overset{\Delta}{=}\{ \begin{matrix}\max\limits_{{\{ F_{m}\}},b} & b \\{{s.t.\; {\overset{\_}{sinr}}_{m}^{k}} \geq a} & {{\forall m},k,} \\{\frac{{F_{m}}_{2}}{\sqrt{P_{m}}} \leq b} & {\forall m}\end{matrix} } & (15)\end{matrix}$

The result in Theorem 1 can be extended for a multiuser cell setup (K>1)in order to establish the connection S₁(P) and P₁(P,a). The proof issimple and is omitted for brevity.

Theorem 3 For any given power budget P, P₁(P,a) is strictly increasingand continuous in a at any strictly feasible a and is related to S₁(P)via

P ₁(P,S ₁(P))=1.  (16)

Algorithm 1 of Table 1 below solves S₁(P) by solving P₁(P,a) combinedwith a bi-section line search. The optimality of Algorithm 1 and itsconvergence follows from the monotonicity and continuity of P₁(P,a) atany feasible a. Similar to Theorem 2, it can be shown that P₁(P,a) has acomputationally efficient solution.

Theorem 4 Problem P₁(P,a) can be posed as an SDP problem.

The proof of Theorem 4 is also omitted for brevity purposes.

It should be noted that when K=1, S₁(P) is identical to S(P) and anoptimal solution to the latter problem is obtained using Algorithm 1.

TABLE 1 Algorithm 1 - Robust Max-Min SINR Optimization via PowerOptimization (K ≧ 1) 1: Input P and {{tilde over (h)}_(m,n) ^(k),ε_(m,n)^(k)} 2: Initialize a_(min) = 0 and a_(max) = min_(m,k){P_(m)|(||{tildeover (h)}_(m,m) ^(k)||₂ − ε_(m,m) ^(k))⁺|²} 3:  a₀ ← a_(min) 4:  Repeat5:  Solve P₁(P,a₀) and obtain {F_(m)} 6:   if P₁(P,a₀) ≦ 1 7:   a_(min)← a₀ 8:   Else 9:  a_(max) ← a₀ 10:   end if 11:  a₀ ← (a_(min) +a_(max)) / 2 12: until a_(max) − a_(min) ≦ δ 13: Output S₁(P) = a₀ and{F_(m)}

Referring now to FIG. 4, with continuing reference to FIG. 3 and Table1, a detailed method 400 for maximizing the slack variable by solving apower optimization problem in step 312 according to an exemplaryembodiment is illustrated. It should be noted that Algorithm 1 of Table1 can be implemented in the method 400.

Method 400 can begin at step 402 in which the power constraint P, thechannel estimates {{tilde over (h)}_(m,n) ^(k) } available at the basestations in the network and their corresponding uncertainty region radii{ε_(m) ^(k)} can be input or received by a base station. Here, the powerconstraint P can be selected in accordance with design choice and basestation capabilities while the channel estimations {tilde over(h)}_(m,n) ^(k), for example, can be obtained as quantized feedback fromreceiver devices as discussed above with respect to step 302 and can becommunicated between the base stations.

At step 404, the base station can initialize a_(min) and a_(max):a_(min)=0 and a_(amx)=min_(m,k){P_(m)|(∥{tilde over (h)}_(m,m)^(k)∥₂−ε_(m,m) ^(k))⁺|²}.

At step 406, the base station can set a₀ to a_(min).

At step 408, the base station can solve P₁(P, a₀) using (15) with a₀ toobtain a prospective preceding matrix {F_(m)}.

At step 410, the base station can deter nine whether the value a₀ of theslack variable a results in a pre-determined solution of the poweroptimization problem that is correlated to the solution to the slackvariable maximization problem. For example, as noted above P₁(P,a) isrelated to S₁(P) via P₁(P,S₁(P))=1, where the pre-determined solutionhere is 1. Thus, the base station may determine whether P₁(P,a₀)≦1. IfP₁(P,a₀) then the method may proceed to step 414 in which the basestation can set a_(min) to a₀. Otherwise, the method may proceed to step412, in which the base station can set a_(max) to a₀.

Thereafter, the method may proceed to step 416, in which the basestation can set a₀=(a_(min)/a_(max))/2. Next, the method may proceed tostep 418 in which the base station may determine whethera_(max)−a_(min)≦δ for some δ, which is selected based on design choice.If a_(max)−a_(min)≦δ, then the method may end at step 420, in which thebase station may obtain the solution to the power optimization problemas S₁(P)=a₀ and {F_(m)}. Otherwise, the method may proceed to step 408and repeat.

Solving Via MSE Optimization

As an alternative to solving via power optimization, the precodingmatrices may be determined using MSE optimization. For example, inmethod 300 of FIG. 3, exemplary embodiments can determine the precodingmatrices at step 304 by minimizing, at step 320, a maximum mean squareerror within a set of mean square errors corresponding to the boundedset of channel estimation errors. Further, the minimization of the MSEcan be implemented by minimizing a slack variable correlated to an upperbound of the mean square error. For example, the robust max-min rateoptimization problem can be transformed into a robust min-max MSEoptimization problem by using the fact that

${{MSE}_{m}^{k} = \frac{1}{1 + {SINR}_{m}^{k}}},$

where, as indicated above, MSE_(m) ^(k) is the MSE of user U_(m) ^(k)when it deploys the MMSE equalizer. Consequently, the worst-case MSEcorresponding to the worst-case SINR is given as

$\begin{matrix}{{\max\limits_{\{ D_{m,n}^{k}\}}{MSE}_{m}^{k}} = {\frac{1}{1 + {\min\limits_{\{ D_{m,n}^{k}\}}{SINR}_{m}^{k}}} = {\frac{1}{1 + {sinr}_{m}^{k}}.}}} & (17)\end{matrix}$

Here, the term

$\max\limits_{\{ D_{m,n}^{k}\}}{MSE}_{m}^{k}$

corresponds to the maximum mean square error within a set of mean squareerrors corresponding to a bounded set of channel estimation errors{D_(m,n) ^(k)}. In addition, by recalling the problem S(P) given in (6)and taking into account the representation of MSE_(m) ^(k) given in (4)and the worst-case MSE given in (17), the robust max-min rateoptimization problem can be solved by equivalently solving

$\begin{matrix}{{S_{2}(P)} \equiv \{ \begin{matrix}{\min\limits_{\{ F_{m}\}}{\max\limits_{k,m}{\max\limits_{\{ D_{m,n}^{k}\}}{\min\limits_{f_{m}^{k}}{M\overset{\sim}{S}E_{m}^{k}}}}}} \\{{s.t.\; {F_{m}}_{2}^{2}} \leq {P_{m}\mspace{11mu} {\forall{m.}}}}\end{matrix} } & (18)\end{matrix}$

An upper bound on S₂(P), which in turn results in a lower bound on S(P),can be found and employed to facilitate solving (18). By invoking theinequality

$\begin{matrix}{{\max\limits_{\{ D_{m,n}^{k}\}}{\min\limits_{f_{m}^{k}}{M\overset{\sim}{S}E_{m}^{k}}}} \leq {\min\limits_{f_{m}^{k}}{\max\limits_{\{ D_{m,n}^{k}\}}{M\overset{\sim}{S}E_{m}^{k}}}}} & (19)\end{matrix}$

and introducing the slack variable aεR⁺, the following upper bound S₂(P) on S₂(P) can be obtained as follows:

$\begin{matrix}{{{\overset{\_}{S}}_{2}(P)} \equiv \{ \begin{matrix}{\min\limits_{{\{{F_{m},f_{m}^{k}}\}},a}\;} & a \\{{s.t.\; {\max\limits_{\{ D_{m,n}^{k}\}}{M\overset{\sim}{S}E_{m}^{k}}}} \leq a^{2}} & {{\forall k},m,} \\{{F_{m}}_{2}^{2} \leq P_{m}} & {\forall m}\end{matrix} } & (20)\end{matrix}$

It should be noted that the base station can implement the MSEminimization at step 320 by formulating and solving, at step 322, ageneralized eigenvalue problem (GEVP). It is shown herein below that theproblem in (20) is equivalent to a generalized eigenvalue problem (GEVP)which can be solved efficiently.

Theorem 5 The problem S ₂(P) can be optimized efficiently as a GEVP.

Theorem 5 can be proven as follows. By recalling (3) and furtherdefining the slack variables {b_(m,n) ^(k)}, the constraints (M{tildeover (S)}E_(m) ^(k)≦a²) can be equivalently presented as follows.

$\quad\{ \begin{matrix}{\sqrt{{\sum\limits_{n}^{\;}( b_{m,n}^{2} )^{2}} + 1} \leq {f_{m}^{k}a}} & {{\forall m},k} \\{{{{h_{m,m}^{k}F_{m}} - {f_{m}^{k}e_{k}}}}_{2} \leq b_{m,m}^{k}} & {{\forall m},k,{\forall{{D_{m,m}^{k}}_{2} \leq ɛ_{m,m}^{k}}},} \\{{{h_{m,n}^{k}F_{n}}}_{2} \leq b_{m,n}^{k}} & {{\forall k},{m \neq n},{\forall{{D_{m,n}^{k}}_{2} \leq ɛ_{m,n}^{k}}},}\end{matrix} $

where e_(k) denotes a length K unit vector having a one in its k^(th)position and zeros elsewhere. Without loss of generality, we haveassumed f_(m) ^(k)εR⁺ as multiplying the vectors w^(m) _(k) with anyunit-magnitude complex scalar will not change the objective or theconstraints of the problem S ₂(P). Next, by applying the SchurComplement lemma the constraints ∥h_(m,m) ^(k)F_(m)−f_(m)^(k)e_(k)∥₂≦b_(m,m) ^(k) for all ∥D_(m,m) ^(k)∥₂≦ε_(m,m) ^(k), can beequivalently stated as

${\begin{bmatrix}b_{m,m}^{k} & {{( {{\overset{\sim}{h}}_{m,m}^{k} + D_{m,m}^{k}} )F_{m}} - {f_{m}^{k}e_{k}}} \\{{( F_{m} )^{H}( {{\overset{\sim}{h}}_{m,m}^{k,k} + D_{m,m}^{k}} )^{H}} - {f_{m}^{k}e_{k}}} & {b_{m,m}^{k}I}\end{bmatrix} \pm 0},{\forall{{D_{m,m}^{k}}_{2} \leq ɛ_{m,m}^{k}}},$

which are equivalently given by

${T_{m}^{k}\overset{\Delta}{=}{\begin{bmatrix}{b_{m,m}^{k} - \lambda_{m,m}^{k}} & {{{\overset{\sim}{h}}_{m,m}^{k}F_{m}} - {f_{m}^{k}e_{k}}} & 0 \\{{( F_{m} )^{H}( {\overset{\sim}{h}}_{m,m}^{k} )^{H}} - {f_{m}^{k}e_{k}^{H}}} & {b_{m,m}^{k}I} & {- {ɛ_{m,m}^{k}( F_{m} )}^{H}} \\0 & {{- ɛ_{m,m}^{k}}F_{m}} & {\lambda_{m,m}^{k}I}\end{bmatrix} \pm {0\mspace{14mu} {\forall m}}}},{k.}$

Similarly it can be shown that the constraints ∥h_(m,n)^(k)F_(n)∥₂≦b_(m,n) ^(k) holding for all ∥D_(m,n) ^(k)∥₂≦ε_(m,n) ^(k)are equivalently given by

${U_{m,n}^{k}\overset{\Delta}{=}{\begin{bmatrix}{b_{m,n}^{k} - \lambda_{m,n}^{k}} & {{\overset{\sim}{h}}_{m,n}^{k}F_{n}} & 0 \\{( F_{n} )^{H}( {\overset{\sim}{h}}_{m,n}^{k} )^{H}} & {b_{m,n}^{k}I} & {- {ɛ_{m,n}^{k}( F_{n} )}^{H}} \\0 & {{- ɛ_{m,n}^{k}}F_{n}} & {\lambda_{m,n}^{k}I}\end{bmatrix} \pm {0{\forall{m \neq n}}}}},{k.}$

Finally, it is noted that the constraint √{square root over(Σ_(n)(b_(m,n) ^(k))²+1)}≦f_(m) ^(k)a is equivalent to V_(m) ^(k)+f_(m)^(k)aI±0, ∀m,k, where

${V_{m}^{k}\overset{\Delta}{=}\begin{bmatrix}0 & b_{m}^{k} & 1 \\( b_{m}^{k} )^{H} & 0 & 0 \\1 & 0 & 0\end{bmatrix}},{\forall m},{k.}$

Consequently, the problem S ₂(P) is equivalent to

$\quad\{ \begin{matrix}{\min\limits_{{\{{F_{m},f_{m}^{k}}\}},b,\lambda,a}a} \\{{{s.t.\mspace{14mu} V_{m}^{k}} + {{f_{m}^{k}{aI}} \pm {0{\forall m}}}},k} \\{{T_{m}^{k} \pm 0},{\forall k},m,} \\{{U_{m,n}^{k} \pm 0},{\forall{m \neq n}},k} \\{{{F_{m}}_{2}^{2} \leq {P_{m}{\forall m}}},}\end{matrix} $

which is a standard form of GEVP.

Limited Cooperation

In certain embodiments, distributed methods can be employed for networksthat do not support full CSI exchange between the BSs. One suchdistributed method is denoted as Algorithm 2 and is provided hereinbelow in Table 2. Algorithm 2 can be utilized in a network in whichlimited information exchange between the BSs is employed. Here, each BScan design its precoders independently of others. The cost incurred forenabling such distributed processing is the degraded performancecompared with the centralized processes discussed herein above.

TABLE 2 Algorithm 2—Distributed Robust Max-Min SINR Optimization  1: form = 1, . . . , M do  2:  Input P_(m)  3:   $\begin{matrix}{{B_{m}\mspace{14mu} {initializes}\mspace{14mu} F_{m}} = {{\frac{P_{m}}{K}\lbrack {\frac{( {\overset{\sim}{h}}_{m,m}^{1} )^{H}}{{{\overset{\sim}{h}}_{m,m}^{1}}_{2}},\ldots \mspace{11mu},\frac{( {\overset{\sim}{h}}_{m,m}^{K} )^{H}}{{{\overset{\sim}{h}}_{m,m}^{K}}_{2}}} \rbrack}\mspace{14mu} {and}\mspace{14mu} {broadcasts}}} \\{W_{m} = {F_{m}F_{m}^{H}}}\end{matrix}\quad$  4: end for  5:  Using F_(m), {W_(n)}_(n≠m), eachB_(m) computes sinr _(m) ^(l), ∀ l  6: Repeat  7:  for m = 1, . . . , Mdo  8:  B_(m) solves S_(1,m) (P) and obtains F_(m) ^(*);  9:  B_(m)broadcasts W_(m) ^(*) = F_(m) ^(*)(F_(m) ^(*))^(H) 10:  Each B_(n), n ≠m, computes sinr _(n) ^(l*), ∀ l based on W_(m) ^(*), F_(n) and{W_(j)}_(j≠m,n) 11:  if min_(l){ sinr _(n) ^(l*)} < min_(l){ sinr _(n)^(l)} then B_(n) sends an error message to B_(m) 12:  if B_(m) receivesno error message then it sets F_(m) ← F_(m) ^(*) and broadcasts anupdate message 13:  Upon receiving the update message each B_(n), n ≠ m,sets W_(m) ← W_(m) ^(*) and updates sinr _(n) ^(l), ∀ l 14:  end for 15:until no further precoder update is possible 16: Output {F_(m)}

An underlying notion of the distributed algorithm in Table 2 is tosuccessively update the precoder of one BS at-a-time while keeping therest unchanged. More specifically, at the m^(th) iteration, allprecoders {F_(n)}_(n≠m) are fixed and only BS B_(m) updates its precoderby maximizing the worst-case smallest rate of the m^(th) cell. Byrecalling (14), the optimization problem solved by B_(m) is given by

$\begin{matrix}{{S_{1,m}(P)}\overset{\Delta}{=}\{ \begin{matrix}{\max\limits_{F_{m},a}a} \\{{{s.t.\mspace{14mu} \sin\limits^{\_}}r_{m}^{k}} \geq {a{\forall k}}} \\{{F_{m}}_{2}^{2} \leq P_{m}} \\{{F_{n}\mspace{14mu} {are}\mspace{14mu} {fixed}\mspace{14mu} {for}\mspace{14mu} n} \neq m}\end{matrix} } & (21)\end{matrix}$

Similar to the approach discussed above with regard to solving via poweroptimization, it can be readily verified that S_(1,m)(P) can be solvedthrough the following power optimization problem,

$\begin{matrix}{{P_{1,m}( {P,a} )}\overset{\Delta}{=}\{ \begin{matrix}{\min\limits_{F_{m},b}b} \\{{{s.t.\mspace{14mu} \sin\limits^{\_}}r_{m}^{k}} \geq {a{\forall k}}} \\{{\frac{{F_{m}}_{2}}{\sqrt{P_{m}}} \leq {b{\forall m}}},} \\{{F_{n}\mspace{14mu} {are}\mspace{14mu} {fixed}\mspace{14mu} {for}\mspace{14mu} n} \neq m}\end{matrix} } & (22)\end{matrix}$

which is connected to the original problem S_(1,m)(P) as follows.

Corollary 1 For any given power budget P, P_(1,m)(P,a) is strictlyincreasing and continuous in a at any strictly feasible a and is relatedto S_(1,m)(P) via

P _(1,m)(P,S _(1,m)(P))=1 for m=1, . . . , M.  (23)

To compute sinr _(m) ^(k) in (13), it is clear that B_(m) needs to knowW_(n)=F_(n)F_(n) ^(H), n≠m. Using {W_(n)}, B_(m) can solve S_(1,m)(P)optimally and obtains F_(m)* through solving P_(1,m)(P,a) in conjunctionwith a linear bi-section search, as shown above in line 8 of Algorithm2. It should be noted that solving S_(1,m)(P) optimizes the minimumworst-case rate locally in the m^(th) cell and does not necessarily leadto a boost in the network utility function. As a result, in an exemplaryembodiment, B_(m) is permitted to update its precoder to F_(m)* only ifsuch update results in a network-wide improvement, as shown in lines9-13 of Algorithm 2.

The successive updates of the precoders can continue until no precodercan be further updated unilaterally. The convergence to such point isguaranteed by noting that Algorithm imposes the constraint that B_(m)update its precoder only if it results in network-wide improvement.

Referring now to FIG. 5 with continuing reference to FIG. 3, a basestation may determine a precoding matrix at step 304 by implementing amethod 500 for successively determining matrices in a network in whichbase stations have limited cooperation. Algorithm 2 described above canbe employed in the method 500, which in turn can be performed at eachbase station. The method 500 may begin at step 502 in which the basestation can perform initialization and can broadcast an indication ofthe preliminary precoding matrix. For example, as indicated above inTable 2, the base station B_(m) may employ a power constraint P_(m) asan input. Further, the base station B_(m) can initialize its precodermatrix F_(m) as

$F_{m} = {\frac{P_{m}}{K}\lbrack {\frac{( {\overset{\sim}{h}}_{m,m}^{1} )^{H}}{{{\overset{\sim}{h}}_{m,m}^{1}}_{2}},\ldots \mspace{14mu},\frac{( {\overset{\sim}{h}}_{m,m}^{K} )^{H}}{{{\overset{\sim}{h}}_{m,m}^{K}}_{2}}} \rbrack}$

and can broadcast W_(m)=F_(m)F_(m) ^(H). The base station can obtain theestimated channels as discussed above with respect to step 302 tocompute the preliminary precoder matrix F_(m).

At step 504, the base station can fix the precoding matrices for theother base stations in the network and can compute a rate indication forusers in its cell. For example, each other base station can perform theinitialization and can broadcast indications of precoders for itsestimated channels as discussed above with regard to step 502. Here, thebase station can receive and compile values of W_(n) broadcast by eachother base station B_(n). In addition, the base station may compute arate indication for users in its cell. For example, as provided above inAlgorithm 2, using F_(m),{W_(n)}_(n≠m), the base station B_(m) cancompute sinr _(m) ^(l), ∀l using equation 13.

At step 506, the base station can maximize a slack variablecorresponding to a lower bound of the minimum worst case rate under apower constraint to determine the precoding matrix. For example, asindicated in Algorithm 2, the base station may solve S_(1,m)(P) in (21)to obtain F_(m)*, where the slack variable a is a lower bound on sinr_(m) ^(k) and where ∥F_(m)∥₂ ²≦P_(m). Similar to the process discussedabove with regard to Algorithm 1, the base station may iteratively solvea power optimization problem to obtain a value of the slack variablethat solves the slack variable maximization problem by determiningwhether slack variable values result in a pre-determined solution of thepower optimization problem that is correlated to the solution to theslack variable maximization problem. For example, the base station maysolve (21) using the power optimization problem (22) and thepredetermined solution (23).

At step 508, the base station can transmit the determined precodingmatrix to the other base stations in the network. For example, the basestation B_(m), can broadcast W_(m)*=F_(m)*(F_(m)*)^(H), as indicatedabove in Algorithm 2.

At step 510, the base station can determine whether the precoding matrixF_(m) ^(*) results in a lower minimum receiver rate at one or more ofthe other base stations. For example, as indicated in Algorithm 2, eachother base station B_(n) can compute sinr _(n) ^(l)*, ∀l using (13)based on W_(m)*,F_(n) and {W_(j)}_(j≠m,n), where F_(n) is determinedpreviously by each base station B_(n) by performing the initializationas mentioned above with regard to step 504 and {W_(j)}_(j≠m,n) isobtained as a result of broadcasts performed by other base stations asmentioned above with regard to step 504. Moreover, as provided inAlgorithm 2, each base station can determine whether min_(l){ sinr _(n)^(l)*}<min_(l){ sinr _(n) ^(l)}, where sinr _(n) ^(l) is computed in thesame manner discussed above with regard to the determination of sinr_(m) ^(l) in step 504, and, if so, can send an error message to basestation B_(m). In turn, if the base station B_(m) does not receive anerror message, then the base station can presume that its precodermatrix F_(m)* does not result in a lower minimum receiver rate atanother base station. If the base station B_(m) does receive an errormessage, then the base station can set presume that its precoder matrixF_(m)* does result in a lower minimum receiver rate at another basestation.

If the base station determines that its precoding matrix does not resultin a lower minimum receiver rate at one or more base stations, then themethod can proceed to step 512 in which the base station can apply thedetermined precoding matrix to the beamforming signals. For example, thebase station B_(m) can apply F_(m)* to the beam forming signals.Thereafter, the method may repeat for another time frame, using F_(m)*and any F_(n)* selected by other base stations performing the method 500in lieu of the corresponding preliminary precoders determined inaccordance with step 502. It should also be noted that at step 512, thebase station B_(m) can broadcast an update message indicating thatF_(m)* has been applied by base station B_(m). In turn, the other basestations B_(n) can receive the update message and can set W_(m) toW_(m)*.

If the base station determines that its precoding matrix results in alower minimum receiver rate at one or more base stations, then themethod can proceed to step 514 in which the base station can apply acurrent precoding matrix to the beamforming signals. For example, thecurrent precoding matrix may be the precoding matrix F_(m) that can bedetermined at step 502. Alternatively, the current precoding matrix maybe F_(m)* that was selected in a previous iteration of method 500.Thereafter, the method may repeat for another time frame, using thecurrent precoding matrix and any F_(n)* or W_(n)* selected by other basestations performing the method 500 in lieu of the correspondingpreliminary precoders previously determined in accordance with, forexample, step 502.

It should be noted that in accordance with exemplary aspects, avariation of Algorithm 2 can be employed. In this variation, instead offixing {1, . . . , M} as the order of processing, where the order inwhich the BSs attempt to update their precoders is fixed, as done inAlgorithm 2 described above, a greedy approach can be applied. Inparticular, at each iteration of the process, each BS can compute itsprecoder, assuming precoders of other BSs to be fixed. Then, in abidding phase, each BS can broadcast its choice and only the choicewhich maximizes the network minimum worst-case rate is accepted by allBSs.

Referring now to FIG. 6 with continuing reference to FIGS. 3 and 5, abase station may determine a precoding matrix at step 304 byimplementing a method 600 for greedily determining matrices in a networkin which base stations have limited cooperation. Steps 602-608 areidentical to corresponding, respective steps 502-508 of method 500except that the method steps 602-608 are performed simultaneously byeach base station in the network (or a subset of base stations in thenetwork).

At step 610, each base station B_(n) can determine whether itsdetermined precoding matrix, such as F_(n)*, maximizes a lowest minimumreceiver rate among the base stations. For example, each base stationB_(m) may independently determine sinr _(n) ^(l)*, ∀l,n for eachprecoding matrix F_(n)* received from each base station B_(n) and forits corresponding precoding matrix F_(n=m)*. Thereafter, using thevalues of sinr _(n) ^(l)*, ∀l,n, each base station can determine whichprecoding matrix F_(n)* maximizes the network minimum worst-case rateand can apply that precoding matrix in lieu of the correspondingprecoding matrix F_(n) to update its corresponding sinr _(m) ^(l).

If the base station B_(m) determines that its determined precodingmatrix maximizes a lowest minimum receiver rate among the base stations,then the method can proceed to step 612 in which the base station canapply the determined precoding matrix to the beamforming signals. Forexample, the base station B_(m) can apply F_(m)* to the beam formingsignals. Thereafter, the method may repeat for another time frame, usingF_(m)* and any F_(n)* selected by other base stations performing themethod 600 in lieu of the corresponding preliminary precoders determinedin accordance with step 602.

If the base station determines that its precoding matrix does notmaximize a lowest minimum receiver rate among the base stations, thenthe method can proceed to step 614 in which the base station can apply acurrent precoding matrix to the beamforming signals. For example, thecurrent precoding matrix may be the precoding matrix F_(m) that can bedetermined at step 602. Alternatively, the current precoding matrix maybe F_(m)* that was selected in a previous iteration of method 600.Thereafter, the method may repeat for another time frame, using thecurrent precoding matrix and any F_(n)* selected by other base stationsperforming the method 600 in lieu of the corresponding preliminaryprecoders previously determined in accordance with, for example, step602.

According to other exemplary aspects, another distributed algorithm thatcan optimally solve the optimization problem in (14) can be employed bybase stations. Here, the distributed process may solve the optimizationproblem in (14) by introducing more auxiliary variables and using dualdecomposition. This process involves a higher level of inter-BSsignaling than the previous distributed procedures discussed above.

First, (14) can be rewritten as

$\begin{matrix}\{ \begin{matrix}{\max\limits_{{\{{F_{m},\beta_{m,n}^{k}}\}},a}a} \\{{{s.t.\mspace{14mu} \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}{h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,n}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}( \beta_{m,n}^{k} )^{2}} + 1}\end{matrix}}} \geq {a{\forall m}}},k,} \\{{F_{m}}_{2}^{2} \leq {P_{m}{\forall m}}} \\{{{\max\limits_{D_{m,n}^{k}}{h_{m,n}^{k}{F_{n}( F_{n} )}^{H}( h_{m,n}^{k} )^{H}}} \leq {( \beta_{m,n}^{k} )^{2}{\forall k}}},{m \neq n}}\end{matrix}  & (24)\end{matrix}$

To solve (24), a bi-section search over a can be employed in which forany fixed a, the following problem can be solved

$\begin{matrix}\{ \begin{matrix}{\max\limits_{\{{F_{m},\beta_{m,n}^{k}}\}}{\sum\limits_{m}^{\;}{F_{m}}_{2}^{2}}} \\{{{s.t.\mspace{14mu} \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}{h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,n}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}( \beta_{m,n}^{k} )^{2}} + 1}\end{matrix}}} \geq {a{\forall m}}},k,} \\{{F_{m}}_{2}^{2} \leq {P_{m}{\forall m}}} \\{{{\max\limits_{D_{m,n}^{k}}{h_{m,n}^{k}{F_{n}( F_{n} )}^{H}( h_{m,n}^{k} )^{H}}} \leq {( \beta_{m,n}^{k} )^{2}{\forall{m \neq n}}}},{k.}}\end{matrix}  & (25)\end{matrix}$

Thus, returning to FIG. 3, precoding matrices may be determined bymaximizing a slack variable at step 310, in which the slack variable isgiven by in a (24). In turn, step 310 can be implemented by iterativelyminimizing, at step 350, a sum of norms of precoding matrices usingfixed values of the slack variable in accordance with, for example,(25). In addition, step 350 can be performed by a base station using adual composition approach.

For example, for each BS m, variables β_(m,n) ^(k,m),β_(n,m) ^(j,m)which denote its copies of β_(m,n) ^(k),β_(n,m) ^(j), respectively, canbe defined. Also, let b^((m)) be the vector formed by collecting allsuch variables. Then, (25) can be written as

$\begin{matrix}\{ \begin{matrix}{\min\limits_{\{{F_{m},b^{(m)}}\}}{\sum\limits_{m}^{\;}{F_{m}}_{2}^{2}}} \\{{{s.t.\mspace{14mu} \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}{h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,n}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}( \beta_{m,n}^{k} )^{2}} + 1}\end{matrix}}} \geq {a{\forall m}}},k,} \\{{{\max\limits_{D_{m,n}^{j}}{h_{n,m}^{j}{F_{m}( F_{m} )}^{H}( h_{n,m}^{k} )^{H}}} \leq {( \beta_{n,m}^{j,m} )^{2}{\forall j}}},{n \neq m}} \\{{F_{m}}_{2}^{2} \leq {P_{m}{\forall m}}} \\{{\beta_{m,n}^{k,m} = {\beta_{m,n}^{k,n}{\forall k}}},{m \neq {n.}}}\end{matrix}  & (26)\end{matrix}$

Similar to the proof of Theorem 4, it can be shown that (26) isequivalent to a (convex) SDP and thus strong duality holds for (26)provided Slater's condition is also satisfied. Thus, dual variables{λ_(m,n) ^(k)} can be defined and I can denote the vector formed bycollecting all such variables. The following partial Lagrangian isconsidered

$\begin{matrix}{{L( {\{ {F_{m},b^{(m)}} \},I} )}\overset{\Delta}{=}{{\sum\limits_{m}^{\;}{F_{m}}_{2}^{2}} + {\sum\limits_{m \neq n}^{\;}{\sum\limits_{k}^{\;}{\lambda_{m,n}^{k}( {\beta_{m,n}^{k,m} - \beta_{m,n}^{k,n}} )}}}}} & (27)\end{matrix}$

and the dual function

$\begin{matrix}{{g(I)}\overset{\Delta}{=}\{ \begin{matrix}{{\min\limits_{\{{F_{m},b^{(m)}}\}}{\sum\limits_{m}^{\;}{F_{m}}_{2}^{2}}} + {\sum\limits_{m \neq n}^{\;}{\sum\limits_{k}^{\;}{\lambda_{m,n}^{k}( {\beta_{m,n}^{k,m} - \beta_{m,n}^{k,n}} )}}}} \\{{{s.t.\mspace{14mu} \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}{h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,n}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}( \beta_{m,n}^{k} )^{2}} + 1}\end{matrix}}} \geq {a{\forall m}}},k,} \\{{{\max\limits_{D_{n,m}^{j}}{h_{n,m}^{j}{F_{m}( F_{m} )}^{H}( h_{n,m}^{j} )^{H}}} \leq {( \beta_{n,m}^{j,m} )^{2}{\forall j}}},{n \neq m}} \\{{F_{m}}_{2}^{2} \leq {P_{m}{\forall m}}}\end{matrix} } & (28)\end{matrix}$

The dual problem splits into M smaller problems of the form

$\begin{matrix}\{ \begin{matrix}{{\min\limits_{\{{F_{m},b^{(m)}}\}}{F_{m}}_{2}^{2}} + {\sum\limits_{n:{n \neq m}}^{\;}( {{\sum\limits_{k}^{\;}{\lambda_{m,n}^{k}\beta_{m,n}^{k,m}}} - {\sum\limits_{j}^{\;}{\lambda_{n,m}^{j}\beta_{n,m}^{j,m}}}} )}} \\{{{s.t.\mspace{14mu} \frac{{( {{{{\overset{\sim}{h}}_{m,m}^{k}w_{m}^{k}}} - {ɛ_{m,m}^{k}{w_{m}^{k}}_{2}}} )^{+}}^{2}}{\begin{matrix}{{\max\limits_{D_{m,m}^{k}}{h_{m,m}^{k}{Y_{m,k}( Y_{m,k} )}^{H}( h_{m,n}^{k} )^{H}}} +} \\{{\sum\limits_{n \neq m}^{\;}( \beta_{m,n}^{k,m} )^{2}} + 1}\end{matrix}}} \geq {a{\forall k}}},} \\{{{\max\limits_{D_{n,m}^{j}}{h_{n,m}^{j}{F_{m}( F_{m} )}^{H}( h_{n,m}^{j} )^{H}}} \leq {( \beta_{n,m}^{j,m} )^{2}{\forall j}}},{n:{n \neq m}}} \\{{F_{m}}_{2}^{2} \leq {P_{m}.}}\end{matrix}  & (29)\end{matrix}$

Using the arguments provided in the preceding sections, each of thesmaller problems in (29) can be shown to be equivalent to an SDP.Invoking the strong duality, the primal optimal solution can berecovered by solving the dual problem max_(I){g(I)}. The latter problemcan be also solved in a distributed manner via the sub-gradient method.In particular, suppose {{tilde over (b)}^((m))} are the optimizedvariables obtained upon solving the decoupled optimization problems in(29). Then the dual variables can be updated using a sub-gradient asλ_(m,n) ^(k)→λ_(m,n) ^(k)+μ({tilde over (β)}_(m,n) ^(k,m)−{tilde over(β)}_(m,n) ^(k,m)), ∀k,m≠n, where μ is a positive step size parameter.Updating λ_(m,n) ^(k) involves exchanging {tilde over (β)}_(m,n)^(k,m),{tilde over (β)}_(m,n) ^(k,n) between BSs m,n, respectively.Finally, it should be noted that a speed-up can be obtained at the costof some sub-optimality by forcing equality after a few steps of thesub-gradient method. In particular, both β_(m,n) ^(k,m),β_(m,n) ^(k,n)can be set to be equal to

$\frac{{\hat{\beta}}_{m,n}^{k,m} + {\hat{\beta}}_{m,n}^{k,n}}{2}$

for all k,m≠n and then the M decoupled problems in (26) can beconcurrently optimized over {F_(m)}. The current choice of a can bedeclared feasible if and only if all of the problems are feasible.

Robust Weighted Sum-Rate Optimization

Referring now to FIG. 7 with continuing reference to FIGS. 1-3, anexemplary method 700 for optimizing the utility of receiver devices in awireless communication system is illustrated. Here, the method 700generally implements a robust weighted sum-rate optimization and can beperformed in a variety of ways, as discussed further herein below.Similar to method 300, method 700 can be performed by a base station102, 202 or a control center if full cooperation between base stationsis feasible. Alternatively, if only limited cooperation is feasible,then the method can be performed independently by each base station, asdiscussed further herein below.

The method 700 may begin at step 702 in which a base station or acontrol center may obtain a set of estimates of channel statescorresponding to channels received by a set of receiver devices. Theseestimates can be received in the form of feedback from the receiverdevices. The feedback from each receiver device can be limited to a fewbits. Thus, each receiver device may have to quantize each one of thechannel estimates available to it. Consequently, the base station orcontrol center has access to channel estimates that can be corrupted byquantization errors. As indicated above, the channel state informationmay correspond to {h_(m,n) ^(k)}_(n=1) ^(M), which includes the channelreceived by the receiver from the base station B_(m) servicing thereceiver in addition to the channels h_(m,n) ^(k) (n≠m) received by thereceiver from base stations B_(n) servicing cells other than cell m. Ina fully cooperative scenario, each base station may communicate therespective channel state information for receivers or users in thecorresponding cell to the other base stations in the network or to acontrol center. Alternatively, in a limited cooperation scenario, whileeach base station can estimate channel information {h_(m,n) ^(k)}_(n=1)^(M) for receivers in its own cell, each base station can communicateprospective precoding matrices and equalizer matrices to other bases tooptimize receiver utility in the network, as discussed in more detailherein below.

At step 704, a base station or control center can determine a precodingmatrix for the set of receiver devices by maximizing a worst-caseweighted sum rate of the network. For example, as discussed above withregard to equation (7), the precoding matrices {F_(m)} for the basestations in the network can be determined in accordance with a powerbudget by solving the following:

${R(P)}\overset{\Delta}{=}\{ \begin{matrix}{\max\limits_{\{ F_{m}\}}{\min\limits_{\{ D_{m,n}^{k}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}R_{m}^{k}}}}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall{m.}}}}\end{matrix} $

Here the term

$\min\limits_{\{ D_{m,n}^{k}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}R_{m}^{k}}}}$

is an example of the minimum receiver weighted sum-rate which is withina set of rates corresponding to a bounded set of channel estimationerrors {D_(m,n) ^(k)}. The weights α_(m) ^(k) can be employed toimplement a user-priority scheme, where the rates R_(m) ^(k) of usersU_(m) ^(k) with a higher priority are given higher corresponding weightsα_(m) ^(k). Furthermore, the estimate can be determined by considering aset of channels {h_(m,n) ^(k)}_(n=1) ^(M) including channels h_(m,n)^(k) (n≠m) received by the set of receiver devices from base stationsB_(n) (n≠m) other than a base station B_(m) servicing the set ofreceiver devices. For example, as noted below with respect to variousexemplary embodiments, {h_(m,n) ^(k)}_(n=1) ^(M) can be used todetermine values of M{tilde over (S)}E_(m) ^(k) to solve equation (7).

At step 706, each base station can transmit beamforming signalsgenerated in accordance with the determined precoding matrix to theirown respective receiver devices in their respective cell.

It should be noted that, at step 704, in the full cooperation scenario,a control center can determine precoding matrices for each base stationusing channel state information for receivers in each cell serviced bythe base stations and can assign the precoding matrices to the basestations to enable them to generate optimized, beamforming signals fortransmission to the receiver. Alternatively, in the full cooperationscenario, each base station may independently determine their ownprecoding matrix using the same methods, where each base station canreceive channel state information from other base stations, determinethe precoding matrices for the entire network and apply a correspondingprecoding matrix for its own beamforming signals. In addition, one ormore base stations can implement step 704 by solving equation (7) viaMSE optimization. Exemplary embodiments that solve (7) using MSEoptimization in a full cooperation scenario and a limited cooperationscenario are described herein below.

Full Cooperation

By recalling the definitions in (5) and taking into account that theuncertainty regions corresponding to SINR_(m) ^(k) and SINR_(n) ^(l) form≠n or l≠k are disjoint, finding the worst-case SINR for each user canbe carried out independently of the rest. Hence, by recalling that theworst-case SINR of user U_(m) ^(k) is denoted by sinr_(m) ^(k), theproblem R(P) in (7) is given by

$\begin{matrix}{{R(P)} = \{ \begin{matrix}{\max\limits_{\{ F_{m}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}{\log ( {1 + {\sin \; r_{m}^{k}}} )}}}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall m}}}\end{matrix} } & (30)\end{matrix}$

The problem R(P) as posed above, is not a convex problem. Optimalprecoder design based on maximizing the weighted sum-rate even when theBSs have perfect CSI is intractable. To the best of the knowledge of theinventors, even in this case only techniques yielding locally optimalsolutions can be used. According to exemplary aspects of the presentinvention, a suboptimal solution can be found by obtaining aconservative approximation of the problem R(P). This approximationprovides a lower bound on R(P).

To start the set of functions {S_(m) ^(k)(u): R→R} is defined as

${{S_{m}^{k}(u)}\overset{\Delta}{=}{{\alpha_{m}^{k}u} - {\frac{\alpha_{m}^{k}}{1 + {\sin \; r_{m}^{k}}}{\exp ( {u - 1} )}}}},{{{for}\mspace{14mu} m} = 1},\ldots \mspace{14mu},M,{k = 1},\ldots \mspace{14mu},K,{{{and}\mspace{14mu} u} \in {R.}}$

It can be readily verified that

${\max\limits_{u \in R}{S_{m}^{k}(u)}} = {\alpha_{m}^{k}{\log ( {1 + {\sin \; r_{m}^{k}}} )}}$

and

$\begin{matrix}{{u^{*} = {{\arg \; {\max\limits_{u \in R}{S_{m}^{k}(u)}}} = {{\log ( {1 + {\sin \; r_{m}^{k}}} )} + {1{\forall k}}}}},{m.}} & (31)\end{matrix}$

Therefore, by incorporating the slack variables u=[u_(m) ^(k)] andsubstituting the objective function of R(P) with its equivalent termΣ_(m=1) ^(M)Σ_(k=1) ^(K)max_(u) _(m) _(k) S_(m) ^(k)(u_(m) ^(k)), theproblem R(P) is equivalently given by

$\begin{matrix}{{R(P)} = \{ \begin{matrix}{{\max\limits_{{\{ F_{m}\}},u}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}u_{m}^{k}}}}} - {\frac{\alpha_{m}^{k}}{1 + {\sin \; r_{m}^{k}}}{\exp ( {u_{m}^{k} - 1} )}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall m}}}\end{matrix} } & (32)\end{matrix}$

For any fixed u the intermediate problem {tilde over (R)}(P,u) isdefined, where {tilde over (R)}(P,u) yields the optimal precoders{F_(m)} corresponding to the given u and power budget P. Since for agiven u the term Σ_(m)Σ_(k)α_(m) ^(k)u_(m) ^(k) becomes a constant, thefollowing is obtained

$\begin{matrix}{{\overset{\sim}{R}( {P,u} )}\overset{\Delta}{=}\{ \begin{matrix}{\min\limits_{\{ F_{m}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\frac{\alpha_{m}^{k}}{1 + {\sin \; r_{m}^{k}}}{\exp ( {u_{m}^{k} - 1} )}}}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall m}}}\end{matrix} } & (33)\end{matrix}$

The problem {tilde over (R)}(P,u) can now be transformed into a weightedsum of the worst-case MSEs as follows,

$\begin{matrix}{{\overset{\sim}{R}( {P,u} )}\overset{\Delta}{=}\{ \begin{matrix}{\min\limits_{\{ F_{m}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}{\exp ( {u_{m}^{k} - 1} )}{\max\limits_{\{ D_{m,n}^{k}\}}{\min\limits_{f_{m}^{k}}{M\overset{\sim}{S}E_{m}^{k}}}}}}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall m}}}\end{matrix} } & (34)\end{matrix}$

Next, for any given u an upper bound on {tilde over (R)}(P,u) can befound, where, by recalling (32) and (34), the upper bound on {tilde over(R)}(P,u) is a lower bound on R(P). By invoking the inequality in (19)and defining f_(m)=[f_(m) ^(k)]_(k), an upper bound on {tilde over(R)}(P,u) can be found as follows,

$\begin{matrix}{{\overset{\_}{R}( {P,u} )}\overset{\Delta}{=}\{ \begin{matrix}{\min\limits_{\{{F_{m},f_{m}}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}{\exp ( {u_{m}^{k} - 1} )}{\max\limits_{\{ D_{m,n}^{k}\}}{M\overset{\sim}{S}E_{m}^{k}}}}}}} \\{{s.t.\mspace{14mu} {F_{m}}_{2}^{2}} \leq {P_{m}{\forall m}}}\end{matrix} } & (35)\end{matrix}$

R(P,u) can itself be sub-optimally solved by using the alternatingoptimization (AO) principle and optimizing {f_(m)} and {F_(m)} in analternating manner. By deploying AO {F_(m)} can be optimized whilekeeping {f_(m)} fixed and vice versa. Since the objective is bounded andit decreases monotonically at each iteration, the AO procedure isguaranteed to converge. In the following theorem it is shown thatsolving R(P,u) at each step of the AO procedure is a convex problem witha computationally efficient solution.

Theorem 6 For arbitrarily fixed {F_(m)}, the problem R(P,u) can beoptimized over {f_(m)} efficiently as an SDP. Similarly, for arbitrarilyfixed {f_(m)}, the problem R(P,u) can be optimized over {F_(m)}efficiently as another SDP.

The proof of Theorem 6 is as follows. It is first shown that for anygiven and fixed {f_(m) ^(k)}, the problem R(P,u) is equivalent to anSDP. q_(m) ^(k) is defined as q_(m) ^(k)=α_(m) ^(k)exp(u_(m)^(k)−1)/|f_(m) ^(k)|². Accordingly,

${\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}{\exp ( {u_{m}^{k} - 1} )}M\overset{\sim}{S}E_{m}^{k}}}} = {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}\underset{\underset{\equiv {g{(w_{m}^{k})}}}{}}{( {{q_{m}^{k}{{{h_{m,m}^{k}w_{m}^{k}} - f_{m}^{k}}}^{2}} + {\sum\limits_{l \neq k}^{\;}{q_{m}^{l}{{h_{m,m}^{l}w_{m}^{k}}}^{2}}} + {\sum\limits_{n \neq m}^{\;}{\sum\limits_{l}^{\;}{q_{n}^{l}{{h_{n,m}^{l}w_{m}^{k}}}^{2}}}}} )}}}$

Clearly, the optimization of R(P,u) now decouples into M optimizationproblems of the form

$\begin{matrix}\{ \begin{matrix}{\min\limits_{F_{m},b_{m}}b_{m}} \\{{s.t.\mspace{14mu} {\max\limits_{\{ D_{n,m}^{i}\}}{\sum\limits_{k = 1}^{K}{g( w_{m}^{k} )}}}} \leq b_{m}} \\{{F_{m}}_{2}^{2} \leq {P_{m}{\forall{m.}}}}\end{matrix}  & (36)\end{matrix}$

It can be verified that the constraints can be equivalently expressed asfinitely many Linear Matrix Inequalities (LMIs) so that the optimizationproblem is equivalent to an SDP. Next, suppose {F_(m)} are arbitrarilyfixed. Then R(P,u) reduces to

$\min\limits_{\{ f_{m}^{k}\}}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\alpha_{m}^{k}{\exp ( {u_{m}^{k} - 1} )}{\max\limits_{\{ D_{m,n}^{k}\}}{M\overset{\sim}{S}E_{m}^{k}}}}}}$

The above optimization problem decouples into KM smaller problems of theform

$\begin{matrix}{\min\limits_{\{ f_{m}^{k}\}}{\max\limits_{\{ D_{m,n}^{k}\}}{M\overset{\sim}{S}E_{m}^{k}}}} & (37)\end{matrix}$

Substituting g_(m) ^(k)=1/f_(m) ^(k) in (37), the optimization caninstead be performed over g_(m) ^(k) and the latter optimization problemcan be readily shown to be equivalent to an SDP.

TABLE 3 Algorithm 3 - Robust Weighted Sum-rate Optimization 1: Input Pand {{tilde over (h)}_(m,n) ^(k),ε_(m,n) ^(k)} 2: Initialize F_(m),f_(m)for all m and u 3: Repeat 4: Solve R(P,u) by optimizing over {F_(m)} and{f_(m) ^(k)} in an alternating manner; 5: Update u_(m) ^(k) ← 1 −log(max {D_(m,n) ^(k)} M{tilde over (S)}E_(m) ^(k)),∀k,m 6: untilconvergence 7: Output {F_(m)}

Algorithm 3 provided in Table 3 above summarizes steps that can beemployed for sub-optimally solving R(P). Algorithm 3 is constructedbased on the connection between the objective functions of R(P) andR(P,u). At each iteration of Algorithm 3 for a fixed u, R(P,u) is solvedby using the AO principle as discussed above and a new set of precodersand equalizers is obtained. The minimum rate achieved by using this setof precoders and equalizers provides a lower bound on R(P). This set ofprecoders and equalizers is also deployed for computing the worst-caseMSEs and updating u as u_(m) ^(k)=1−log(max_({D) _(m,n) _(k) _(})M{tildeover (S)}E_(m) ^(k)),∀k,m. It should be noted that the worst-case MSEscan be computed using the techniques described above with respect to theproof of Theorem 5.

Since R(P) is bounded from above, so is any lower bound on it.Therefore, the utility function of Algorithm 3 is bounded and increasesmonotonically in each iteration. Thus, convergence of Algorithm 3 isguaranteed.

With reference now to FIG. 8 with continuing reference to FIG. 7, anexemplary method 800 for determining precoding matrices and equalizersthat optimize the utility of receivers in a wireless communicationnetwork in accordance with an exemplary embodiment is illustrated.Algorithm 3 can be employed in the method 800 to determine the precodersand the equalizers. Further, the method 800 can be performed by one ormore base stations, including a control center, and can be performed toimplement step 704 of the method 700. Alternatively, each base stationmay implement a distributed variation of the method 800, as discussedfurther herein below.

Method 800 can begin at step 802, in which a base station may obtainpowers P, channel estimates {tilde over (h)}m,n^(k), uncertainty regionsε_(m,n) ^(k) and user-weights α_(m) ^(k). The values of these parameterscan be communicated between the base stations.

At step 804, the base station can initialize the slack variables u, thechoice of precoding matrices F_(m) and the equalizers f_(m) for all mbase stations using the variables obtained at step 802.

At step 806, the base station may obtain the worst-case weighted MSEformulation. For example, the base station may obtain the formulation(35).

At step 808, the base station can iteratively solve the formulation overa set of precoding matrices and a set of equalizers by alternatelyoptimizing the formulation for the set of precoding matrices with afixed equalizer matrix and optimizing the formulation for the set ofequalizer matrices with a fixed precoding matrix until convergence ofthe formulation to obtain a new set of optimal precoding matrices and anew set optimal equalizer matrices for the base stations. For example,holding u fixed, the base station may solve or optimize R(P,u) over theset of precoding matrices {F_(m)} and the equalizers {f_(m)} in analternating manner by solving corresponding SDPs until the formulationconverges. The new precoding matrices {F_(m)} and the equalizers {f_(m)}can be obtained to update the slack variables and to determine whetherthe formulation has converged to the maximum MSE.

At step 810, the base station may update the slack variables with theprecoding matrices and new equalizers. For example, the base station mayupdate the slack variables u by setting the precoders and equalizers tothe new precoding matrices and new equalizers and holding them fixed.The slack variable may be updated by using the closed from expression,where u_(m) ^(k) can be set to 1−log(max_({D) _(m,n) _(k) _(})M{tildeover (S)}E_(m) ^(k)) for all m, k.

At step 812, the base station may compute the worst-case weighted sumrate with the new sets of precoding matrixes and equalizer matrices. Forexample, as indicated above, the procedure set forth in the proof ofTheorem 5 can be employed to find the worst-case weighted sum rate.

At step 814, the base station can determine whether the worst case ratedsum-rate has converged. If the worst-case rated sum-rate has converged,then the method may proceed to step 806 and repeat. Otherwise the methodmay proceed to step 816, in which the base station can output the choiceof precoding matrices {F_(m)} and equalizer matrices {f_(m)} that leadto the convergence of the worst-case rated sum rate.

In accordance with an exemplary embodiment, a control center candistribute the precoding matrices {F_(m)} and equalizers {f_(m)} to thecorresponding base stations, each of which may apply its respectiveprecoding matrix F_(m) and equalizer f_(m) to generate beamformingsignals for transmission to its set of receiver devices. For example,each base station may apply its precoding matrix F_(m) and equalizerf_(m) to generate beamforming signals for transmission in step 706 ofmethod 700.

Distributed Implementation

An advantage of the AO based approach employed to sub-optimally solveR(P) in the previous section is that it is amenable to a distributedimplementation. In particular, it should be noted that for fixedu,{f_(m)}, the optimization over {F_(m)} decouples into M smallerproblems (36) that can be solved concurrently by the M BSs. Similarly,for fixed u,{F_(m)}, the optimization over {f_(m)} decouples into KMsmaller problems (37) that can be solved concurrently. Finally, for agiven {F_(m),f_(m)} the elements of u can also be updated concurrently.Consequently, Algorithm 3 and method 800 can indeed be implemented in adistributed fashion with appropriate information exchange among the BSs.

For example, similar to methods 500 and 600 of FIGS. 5 and 6,respectively, each base station m may successively determine itscorresponding precoding matrix and equalizers using Algorithm 3 ormethod 800 by employing a fixed set of channel estimations and a fixedset of equalizers for base stations n≠m. For example, each base stationm can receive channels {h_(n,m) ^(l)} and/or precoding matrices{F_(n≠m)} and {f_(n) ^(l)} from the other base stations in step 802 andmay optimize its precoding matrices F_(m) and equalizers f_(m) in analternating manner using (36) and (37) respectively by holding f_(m) andF_(m) fixed respectively, and holding u fixed as discussed above withregard to step 808. Thereafter, as discussed above with respect to step810, the base station m can update u using the closed form expression.Subsequently, the base station in can iteratively determine theprecoding matrices F_(m) and equalizers f_(m) using alternatingoptimization and can update the slack variable u until the worst-castweighted sum rate has converged, as discussed above with regard to steps812 and 814 of method 800.

In addition, after obtaining the optimized precoding matrices F_(m) andequalizers f_(m), similar to method 500 of FIG. 5, the base station mcan determine whether the optimized precoding matrices F_(m) andequalizers f_(m) result in a higher MSE at any of the other basestations by broadcasting of the precoding matrices F_(m) and equalizersf_(m) and by employing error messages. In addition, the base station canapply the optimized precoding matrices F_(m) and equalizers f_(m) instep 706 to transmit beamforming signals to its set of receivers if theoptimized precoding matrices and equalizers do not result in a higherMSE at any of the other base stations.

In an alternative implementation, after obtaining the optimizedprecoding matrices F_(m) and equalizers f_(m), similar to method 600 ofFIG. 6, the base stations may update the precoders and matrices in agreedy manner using a bidding procedure. For example, similar to step608, each base station may transmit its corresponding optimizedprecoding matrices and equalizers to each other base station and thebase stations may determine that only the precoding matrix F_(m) andequalizers f_(m) resulting in a lowest network-wide MSE will be applied.Thus, the corresponding base station m can apply the precoding matrixF_(m) and equalizers f_(m) to transmit beamforming signals to itsreceivers while the remaining base stations n≠m can update its values ofthe precoders and equalizers employed at base station m. The process maythereafter be repeated for a following time frame.

SLIR Optimization

Another adequate albeit sub-optimal approach for selecting thebeamforming vectors considers the worst-casesignal-to-leakage-interference-plus-noise ratio (SLINR) can be employed.The SLINR metric has been shown to be an effective metric over networkswith perfect CSI. In particular, the worst-case SLINR corresponding touser k in cell m is given by,

$\begin{matrix}{{slinr}_{m}^{k}\overset{\Delta}{=}{\max\limits_{\{ D_{m,n}^{k}\}}\frac{{{h_{m,m}^{k}w_{m}^{k}}}^{2}}{{\sum\limits_{j \neq k}^{\;}{{h_{m,m}^{j}w_{m}^{k}}}^{2}} + {\sum\limits_{n \neq m}^{\;}{\sum\limits_{l}^{\;}{{h_{n,m}^{l}w_{m}^{k}}}^{2}}} + 1}}} & (38)\end{matrix}$

The uncertainty regions in the numerator and denominator are decoupledso that

$\begin{matrix}{{slinr}_{m}^{k} = \frac{\min\limits_{\{ D_{m,m}^{k}\}}{{h_{m,m}^{k}w_{m}^{k}}}^{2}}{{\sum\limits_{j \neq k}^{\;}{\max\limits_{\{ D_{m,m}^{j}\}}{{h_{m,m}^{j}w_{m}^{k}}}^{2}}} + {\sum\limits_{n \neq m}^{\;}{\sum\limits_{l}^{\;}{\max\limits_{\{ D_{n,m}^{l}\}}{{h_{n,m}^{l}w_{m}^{k}}}^{2}}}} + 1}} & (39)\end{matrix}$

If a per-user power profile {P_(m) ^(k)} has been given, then thebeamforming vectors can be independently designed by solving

$\begin{matrix}\{ \begin{matrix}{\max\limits_{w_{m}^{k}}{slinr}_{m}^{k}} \\{{{s.t.\mspace{14mu} {w_{m}^{k}}_{2}^{2}} \leq {P_{m}^{k}{\forall k}}},{m.}}\end{matrix}  & (40)\end{matrix}$

The maximization problem in (40) can be exactly solved by alternativelysolving a power optimization problem in conjunction with a linearbi-section search as described in (14) and (15).

Exemplary embodiments of the present invention discussed herein aboveprovide improved precoder designs that optimize user-rates by accuratelyaccounting for inter-cell interference and intra-cell interference. Byconsidering channel states and corresponding uncertainty regions ofinterference channels received by users, the precoders of exemplaryembodiments can guarantee minimum utility levels for all possible errorsin the uncertainty regions.

It should be understood that embodiments described herein may beentirely hardware, entirely software or including both hardware andsoftware elements. In a preferred embodiment, the present invention isimplemented in hardware and software, which includes but is not limitedto firmware, resident software, microcode, etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

1. In a multiple-input multiple-output (MIMO) wireless communicationssystem including: a first set of one or more mobile stations; a firstbase station that serves the first set of mobile stations; and a secondbase station coordinated with the first base station, a methodimplemented in the first base station, comprising: receiving from eachof the first set of mobile stations a first indication of first channelstate information corresponding to the first base station and a secondindication of second channel state information corresponding to thesecond base station.
 2. The method of claim 1, further comprising:deciding a precoder according to the first and second indicationsreceived from the first set of mobile stations.
 3. The method of claim1, further comprising: receiving from each of a second set of mobilestations a third indication of third channel state informationcorresponding to the first base station and a fourth indication offourth channel state information corresponding to the second basestation.
 4. The method of claim 3, further comprising: deciding aprecoder according to the first and second indications received from thefirst set of mobile stations and the third and fourth indicationsreceived from the second set of mobile stations.
 5. The method of claim1, wherein at least one of the first channel state information and thesecond channel state information comprises a channel estimate.
 6. Themethod of claim 1, wherein at least one of the first channel stateinformation and the second channel state information is quantized ateach of the first set of mobile stations.
 7. The method of claim 3,wherein at least one of the third channel state information and thefourth channel state information comprises a channel estimate.
 8. Themethod of claim 3, wherein at least one of the third channel stateinformation and the fourth channel state information is quantized ateach of the second set of mobile stations.
 9. A base station used in amultiple-input multiple-output (MIMO) wireless communications systemincluding a first set of one or more mobile stations and another basestation coordinated with the base station, the base station comprising:a first receiving unit to receive from each of the first set of mobilestations a first indication of first channel state informationcorresponding to the base station and said a second indication of secondchannel state information corresponding to said another base station,wherein the base station serves the first set of mobile stations. 10.The base station of claim 9, further comprising: a first decision unitto decide a precoder according to the first and second indicationsreceived from the first set of mobile stations.
 11. The base station ofclaim 9, further comprising: a second receiving unit to receive fromeach of a second set of mobile stations a third indication of thirdchannel state information corresponding to the base station and a fourthindication of fourth channel state information corresponding to saidanother base station.
 12. The base station of claim 11, furthercomprising: a second decision unit to decide a precoder according to thefirst and second indications received from the first set of mobilestations and the third and fourth indications received from the secondset of mobile stations.
 13. The base station of claim 9, wherein atleast one of the first channel state information and the second channelstate information comprises a channel estimate.
 14. The base station ofclaim 9, wherein at least one of the first channel state information andthe second channel state information is quantized at each of the firstset of mobile stations.
 15. The base station of claim 11, wherein atleast one of the third channel state information and the fourth channelstate information comprises a channel estimate.
 16. The base station ofclaim 11, wherein at least one of the third channel state informationand the fourth channel state information is quantized at each of thesecond set of mobile stations.